1. Drive and contribute to cutting-edge research projects in NLP/ML.
2. Develop benchmarks and baselines using publicly available datasets.
3. Train large language models (LLMs) on innovative, practical tasks.
4. Publish research findings at leading NLP/ML/AI conferences and in top journals.
1. Currently in the PhD degree in NLP, Machine Learning, or a related area, or recently submitted the thesis; available at least three days per week (with the supervisor and university approval).
2. At least one publication in a top-tier NLP/ML conference or journal (e.g., ACL, NAACL, EMNLP, EACL, NeurIPS, ICLR, ICML).
3. Hands-on experience with Python and deep learning libraries, especially PyTorch, Hugging Face, and Transformers.
4. Experience in one or more of the following areas: supervised fine-tuning of LLMs, LLM self-learning, LLM efficiency, LLM-based AI agents, or LLM applications in gaming, reasoning, or mathematical tasks.
#LI-RL1
